Action recognition in videos is a fundamental task in computer vision. Recently, with the rapid development of deep learning, and in particular, deep convolutional neural networks (CNNs), a number of models [1]} [2]} [3]} [4]} have been proposed for image recognition. However, for video-based action recognition, a model should accept inputs with variable length and generate the corresponding outputs. This special requirement makes the conventional CNN model that caters for one-versus-all classification unsuitable.
